Dane wybrane do predykcji cen złota to statystyki dotyczące sytuacji ekonomicznej, gospodarczej i społecznej na świecie na przestrzeni lat oraz wskaźniki kapitalizacji giełdowej S&P Composite. Z powodu dużej ilości wartości nieznanych oraz faktu, że jeden z najpopularniejszych i najczęściej zalecanych algorytmów - Random Forest nie jest w stanie ich obsłużyć, konieczne były pewne modyfikacje. Z końcowego zbioru danych zostały usunięte parametry zawierające ponad połowę wartości pustych, natomiast akcja obsługi wartości pustych została ustawiona na na.roughfix, co polega na zastąpieniu nieznanych wartości medianą kolumny. Dzięki temu, jesteśmy w stanie oszacować najbardziej wpływowe czynniki, które są powiązane ze zmianą cen złota. W dużej mierze są to parametry, które pośrednio wskazują na upływ lat i starzenie się społeczeństwa (jak np odsetek populacji w wieku 65 lat i powyżej), ale również ogólne wskaźniki poziomu i stylu życia społeczeństwa (jak np PKB, odsetek urodzeń czy odsetek populacji miejskiej).
s_p_composite <- read.csv("Dokumenty/ZED-lab/S&P Composite.csv", header=TRUE) %>%
gather(key = "param", value = "value", 2:10) %>%
mutate(year = as.numeric(substr(Year,1,4)))
s_p_composite <- aggregate(value ~ year + param, data = s_p_composite, mean)
s_p_no_missing_values <- s_p_composite[!is.na(s_p_composite$value), ]
s_p_composite_plot <- ggplot(data = s_p_no_missing_values, mapping = aes(x = year, y = value, color=param)) + geom_line() + geom_point() + facet_wrap(~param, scales = "free", ncol=1)
ggplotly(s_p_composite_plot)
st(s_p_composite %>% spread(param, value))
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| year | 151 | 1946 | 43.734 | 1871 | 1908.5 | 1983.5 | 2021 |
| CPI | 151 | 62.619 | 76.75 | 6.462 | 10.212 | 101.742 | 267.817 |
| Cyclically.Adjusted.PE.Ratio | 141 | 17.237 | 6.994 | 5.311 | 12.003 | 20.774 | 42.068 |
| Dividend | 151 | 6.901 | 12.434 | 0.18 | 0.422 | 7.14 | 59.094 |
| Earnings | 151 | 15.76 | 29.629 | 0.206 | 0.57 | 14.968 | 134.917 |
| Long.Interest.Rate | 151 | 4.501 | 2.295 | 0.894 | 3.219 | 5.049 | 13.911 |
| Real.Dividend | 151 | 17.637 | 11.373 | 5.728 | 9.365 | 22.489 | 62.339 |
| Real.Earnings | 151 | 35.241 | 30.244 | 7.871 | 14.529 | 43.987 | 144.072 |
| Real.Price | 151 | 625.906 | 742.279 | 84.791 | 186.284 | 711.639 | 4172.503 |
| S.P.Composite | 151 | 332.147 | 696.689 | 3.136 | 7.894 | 160.446 | 4114.705 |
s_p_composite_density_plot <- ggplot(s_p_composite, aes(x=value)) + geom_density() + facet_wrap(~param, ncol=1, scales = "free")
ggplotly(s_p_composite_density_plot)
s_p_composite_boxplot_multiple <- ggplot(data=s_p_composite, mapping = aes(x = as.factor(''), y=value, color = param)) + geom_boxplot() + facet_wrap(~param, scales = "free")
ggplotly(s_p_composite_boxplot_multiple)
bchain_diff <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-DIFF.csv") %>%
rename(diff = Value)
bchain_hrate <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-HRATE.csv") %>%
rename(hrate = Value)
bchain_mkpru <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-MKPRU.csv") %>%
rename(mkpru = Value)
bchain_trvou <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-TRVOU.csv") %>%
rename(trvou = Value)
bchain <- merge(x = bchain_diff, y = bchain_hrate, by = "Date")
bchain <- merge(x = bchain, y = bchain_mkpru, by = "Date")
bchain <- merge(x = bchain, y = bchain_trvou, by = "Date")
bchain <- bchain %>% gather(key="param", value="value", 2:5)
bchain$Date <- substr(bchain$Date,1,4)
bchain <- bchain %>%
mutate_at("Date", as.numeric)
bchain <- aggregate(bchain, by=list(bchain$Date, bchain$param), FUN=mean, na.rm=TRUE) %>%
select(-Date, -param) %>%
rename(Year = Group.1, param = Group.2)
chart_bchain <- ggplot(data=bchain, mapping = aes(x=Year, y=value)) + geom_point() + geom_line() + facet_wrap(~param, ncol=1, scales = "free")
ggplotly(chart_bchain)
bchain_density_plot <- ggplot(bchain, aes(x=value)) + geom_density() + facet_wrap(~param, ncol=1, scales = "free")
ggplotly(bchain_density_plot)
st(bchain %>% spread(param, value))
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Year | 13 | 2015 | 3.894 | 2009 | 2012 | 2018 | 2021 |
| diff | 13 | 3957786186336.31 | 6889348202952.88 | 0.987 | 2125246.06 | 4970320545967.22 | 19789690360642.1 |
| hrate | 13 | 28521852.776 | 49255677.911 | 0 | 15.702 | 36394798.569 | 140094812.581 |
| mkpru | 13 | 5854.036 | 12216.739 | 0 | 8.474 | 7362.713 | 44566.454 |
| trvou | 13 | 155486175.602 | 233643226.415 | 0 | 298104.978 | 189145596.269 | 626774501.417 |
bchain_boxplot <- ggplot(data=bchain, mapping = aes(y=value, color=param)) + geom_boxplot()
ggplotly(bchain_boxplot)
bchain_boxplot_multiple <- ggplot(data=bchain, mapping = aes(x = as.factor(''), y=value, color = param)) + geom_boxplot() + facet_wrap(~param, scales = "free") + ylab("Params boxplots")
ggplotly(bchain_boxplot_multiple)
currency_exchange_rates <- read.csv("Dokumenty/ZED-lab/CurrencyExchangeRates.csv", header=TRUE)
currency_exchange_rates <- currency_exchange_rates %>% gather(key = "currency", value = "value", 2:52)
currency_exchange_rates$Date <- substr(currency_exchange_rates$Date,1,4)
currency_exchange_rates <- aggregate(currency_exchange_rates, by=list(currency_exchange_rates$Date, currency_exchange_rates$currency), FUN=mean, na.rm=TRUE) %>% select(-Date, -currency) %>% rename(Year = Group.1, Currency = Group.2)
currency_exchange_no_missing_values <- currency_exchange_rates[!is.na(currency_exchange_rates$value), ]
currency_exchange_rates_plot <- ggplot(data=currency_exchange_no_missing_values, mapping = aes(x=factor(Year), y=value, color=Currency)) + geom_point() + geom_line(aes(group=1)) + scale_x_discrete(breaks = seq(1995, 2018, by = 5))
ggplotly(currency_exchange_rates_plot)
currency_facet_plot <- ggplot(data=currency_exchange_no_missing_values, mapping = aes(x = factor(Year), y = value, color=Currency)) + geom_line(aes(group=1)) + geom_point() + facet_wrap(~Currency, ncol=1, scales = "free") + scale_x_discrete(breaks = seq(1995, 2018, by = 5))
ggplotly(currency_facet_plot)
st(currency_exchange_rates %>% spread(Currency, value))
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Algerian.Dinar | 9 | 91.154 | 17.248 | 72.841 | 77.636 | 109.451 | 114.14 |
| Australian.Dollar | 24 | 0.77 | 0.137 | 0.518 | 0.715 | 0.842 | 1.036 |
| Bahrain.Dinar | 24 | 0.376 | 0 | 0.376 | 0.376 | 0.376 | 0.376 |
| Bolivar.Fuerte | 11 | 2498.607 | 8269.223 | 2.145 | 3.434 | 7.832 | 27431.25 |
| Botswana.Pula | 21 | 0.276 | 0.57 | 0.092 | 0.119 | 0.197 | 2.758 |
| Brazilian.Real | 24 | 2.193 | 0.767 | 0.916 | 1.796 | 2.928 | 3.488 |
| Brunei.Dollar | 21 | 1.502 | 0.193 | 1.25 | 1.366 | 1.69 | 1.792 |
| Canadian.Dollar | 24 | 1.265 | 0.183 | 0.989 | 1.097 | 1.389 | 1.57 |
| Chilean.Peso | 24 | 542.18 | 82.576 | 401.985 | 486.722 | 603.898 | 691.235 |
| Chinese.Yuan | 24 | 7.43 | 0.895 | 6.143 | 6.598 | 8.277 | 8.374 |
| Colombian.Peso | 24 | 2106.72 | 593.404 | 913.61 | 1833.347 | 2534.417 | 3050.151 |
| Czech.Koruna | 18 | 23.193 | 5.41 | 17.032 | 19.565 | 24.563 | 38.022 |
| Danish.Krone | 24 | 6.273 | 0.878 | 5.099 | 5.62 | 6.705 | 8.318 |
| Euro | 21 | 1.207 | 0.161 | 0.896 | 1.109 | 1.328 | 1.471 |
| Hungarian.Forint | 21 | 229.96 | 36.276 | 171.799 | 202.26 | 258.345 | 286.458 |
| Icelandic.Krona | 24 | 92.96 | 24.218 | 62.837 | 70.775 | 117.732 | 131.896 |
| Indian.Rupee | 24 | 48.753 | 9.823 | 32.397 | 43.343 | 54.733 | 67.197 |
| Indonesian.Rupiah | 21 | 9362.046 | 3357.447 | 2248.032 | 8932.033 | 11323.333 | 13632.608 |
| Iranian.Rial | 24 | 12125.235 | 11130.616 | 1747.884 | 1753.242 | 13799.936 | 38039.113 |
| Israeli.New.Sheqel | 18 | 3.984 | 0.407 | 3.48 | 3.602 | 4.397 | 4.738 |
| Japanese.Yen | 24 | 107.881 | 13.471 | 79.771 | 101.969 | 116.665 | 131.081 |
| Kazakhstani.Tenge | 14 | 188.241 | 81.283 | 120.294 | 137.207 | 210.222 | 341.906 |
| Korean.Won | 24 | 1101.035 | 145.608 | 770.901 | 1045.34 | 1167.018 | 1394.534 |
| Kuwaiti.Dinar | 24 | 0.294 | 0.011 | 0.269 | 0.286 | 0.303 | 0.307 |
| Libyan.Dinar | 24 | 1.524 | 0.616 | 0.525 | 0.727 | 1.932 | 1.932 |
| Malaysian.Ringgit | 24 | 3.516 | 0.478 | 2.508 | 3.203 | 3.8 | 4.299 |
| Mauritian.Rupee | 17 | 31 | 2.432 | 27.528 | 29.513 | 31.909 | 35.525 |
| Mexican.Peso | 21 | 12.235 | 3.472 | 6.511 | 10.797 | 13.3 | 18.862 |
| Nepalese.Rupee | 24 | 78.063 | 15.709 | 51.884 | 69.344 | 87.436 | 107.452 |
| New.Zealand.Dollar | 24 | 0.663 | 0.113 | 0.421 | 0.622 | 0.722 | 0.831 |
| Norwegian.Krone | 24 | 6.987 | 1.057 | 5.61 | 6.224 | 7.878 | 8.989 |
| Nuevo.Sol | 9 | 2.975 | 0.281 | 2.637 | 2.753 | 3.235 | 3.371 |
| Pakistani.Rupee | 24 | 71.683 | 24.597 | 31.708 | 56.506 | 95.205 | 112.236 |
| Peso.Uruguayo | 10 | 22.755 | 6.231 | 9.32 | 20.379 | 28.127 | 30.065 |
| Philippine.Peso | 13 | 41.015 | 8.555 | 25.835 | 41.072 | 45.499 | 51.598 |
| Polish.Zloty | 22 | 3.305 | 0.496 | 2.405 | 3.039 | 3.742 | 4.101 |
| Qatar.Riyal | 24 | 3.64 | 0 | 3.64 | 3.64 | 3.64 | 3.64 |
| Rial.Omani | 24 | 0.384 | 0 | 0.384 | 0.384 | 0.384 | 0.384 |
| Russian.Ruble | 16 | 37.65 | 14.433 | 24.869 | 28.694 | 43.435 | 66.849 |
| Saudi.Arabian.Riyal | 24 | 3.749 | 0.002 | 3.745 | 3.748 | 3.75 | 3.75 |
| Singapore.Dollar | 24 | 1.495 | 0.183 | 1.25 | 1.372 | 1.677 | 1.792 |
| South.African.Rand | 24 | 8.22 | 2.891 | 3.627 | 6.447 | 9.861 | 14.702 |
| Sri.Lanka.Rupee | 24 | 104.077 | 30.161 | 51.264 | 85.972 | 128 | 155.171 |
| Swedish.Krona | 24 | 7.741 | 1.028 | 6.495 | 6.841 | 8.309 | 10.326 |
| Swiss.Franc | 24 | 1.2 | 0.25 | 0.888 | 0.979 | 1.372 | 1.69 |
| Thai.Baht | 24 | 35.092 | 5.245 | 24.919 | 31.463 | 40.181 | 44.484 |
| Trinidad.And.Tobago.Dollar | 24 | 6.324 | 0.193 | 5.893 | 6.262 | 6.388 | 6.756 |
| Tunisian.Dinar | 9 | 1.857 | 0.399 | 1.407 | 1.562 | 2.142 | 2.436 |
| U.A.E..Dirham | 24 | 3.672 | 0.001 | 3.671 | 3.672 | 3.672 | 3.673 |
| U.K..Pound.Sterling | 24 | 1.607 | 0.168 | 1.289 | 1.526 | 1.65 | 2.002 |
| U.S..Dollar | 24 | 1 | 0 | 1 | 1 | 1 | 1 |
gold_prices <- read.csv("Dokumenty/ZED-lab/Gold prices.csv", header=TRUE) %>%
gather(key = "currency", value = "value", 2:7) %>%
mutate(year = as.numeric(substr(Date,1,4))) %>%
select(-Date)
gold_prices <- aggregate(value ~ year + currency, data = gold_prices, mean)
gold_prices_plot <- ggplot(data = gold_prices, mapping = aes(x = year, y = value, color = currency)) + geom_line() + geom_point()
ggplotly(gold_prices_plot)
st(gold_prices %>% spread(currency, value))
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| year | 54 | 1994.5 | 15.732 | 1968 | 1981.25 | 2007.75 | 2021 |
| EURO..AM. | 23 | 805.093 | 424.121 | 261.634 | 344.145 | 1121.539 | 1549.454 |
| EURO..PM. | 23 | 804.846 | 424.125 | 261.367 | 343.69 | 1121.811 | 1548.864 |
| GBP..AM. | 54 | 374.948 | 359.867 | 15.012 | 179.256 | 441.393 | 1379.374 |
| GBP..PM. | 54 | 374.769 | 359.748 | 15.006 | 179.158 | 440.367 | 1378.841 |
| USD..AM. | 54 | 580.957 | 498.162 | 35.964 | 282.998 | 828.387 | 1801.001 |
| USD..PM. | 54 | 580.693 | 497.976 | 35.95 | 282.848 | 827.819 | 1800.02 |
gold_prices_boxplot_multiple <- ggplot(data=gold_prices, mapping = aes(x = as.factor(''), y=value, color = currency)) + geom_boxplot() + facet_wrap(~currency, scales = "free", ncol = 2)
ggplotly(gold_prices_boxplot_multiple)
wdi <- read_excel("Dokumenty/ZED-lab/World_Development_Indicators.xlsx") %>%
data.frame() %>%
filter(!is.na(Country.Code))
wdi[wdi == ".."] <- NA
wdi <- wdi %>%
gather("year","value", -Country.Name, -Country.Code, -Series.Name, -Series.Code) %>%
mutate_at("value", as.numeric)
wdi$year <- substr(wdi$year,2,5)
#wdi_by_country <- wdi %>%
# mutate(ID = paste(Country.Code,year)) %>%
# spread(Series.Code,value) %>%
# select(-year)
wdi_by_year_only <- wdi %>%
spread(Series.Name,value) %>%
select(-Country.Code, -Series.Code, -Country.Name)
wdi_by_year_only <- aggregate(wdi_by_year_only, by=list(wdi_by_year_only$year), FUN=mean, na.rm = TRUE) %>%
select(-year) %>%
rename(year = Group.1)
wdi_by_year_only$year <- as.numeric(wdi_by_year_only$year)
#wdi <- aggregate(wdi, by=list(wdi$ID), FUN=mean, na.rm=TRUE) %>%
# select(-Group.1, -Country.Name, -Country.Code, -Series.Name, -ID)
#col<- colorRampPalette(c("blue", "white", "red"))(20)
#heatmap(x = wdi.cor, col = col, symm = TRUE)
gold_prices <- gold_prices %>% filter(currency == 'USD..AM.') %>% select(-currency) %>% rename(gold_price = value)
corr_data <- merge(x = wdi_by_year_only, y = gold_prices, by = "year")
corr_data <- merge(x=corr_data, y = s_p_composite %>% spread(param, value), by = "year") %>%
select(-year)
cor <- cor(corr_data, use = "pairwise.complete.obs")
heatmaply_cor(cor)
cor_table <- as.data.frame(as.table(cor))
biggest_corrs <- subset(cor_table, abs(Freq) > 0.9) %>%
filter(abs(Freq) < 1) %>%
arrange(desc(Freq))
#corr_data$gold_price <- as.factor(corr_data$gold_price)
set.seed(0)
corr_data <- corr_data[, which(colMeans(!is.na(corr_data)) > 0.5)]
split <- createDataPartition(corr_data$gold_price, p = 0.8, list = FALSE)
training <- corr_data[split,]
testing <- corr_data[-split,]
rfGrid <- expand.grid(mtry = 10:30)
control <- trainControl(method = "cv", number = 10)
rfFitTune <- train(gold_price ~ .,
data = training,
method = "rf",
trControl = control,
tuneGrid = rfGrid,
na.action = na.roughfix,
ntree = 30)
rfPred <- predict(rfFitTune , testing)
knitr::kable(postResample(pred = rfPred, obs = testing$gold_price))
## Warning in pred - obs: długość dłuszego obiektu nie jest wielokrotnością
## długości krótszego obiektu
## Warning in pred - obs: długość dłuszego obiektu nie jest wielokrotnością
## długości krótszego obiektu
| x | |
|---|---|
| RMSE | 597.5772 |
| Rsquared | NA |
| MAE | 342.6423 |
most_important_params <- arrange(varImp(rfFitTune)$importance, desc(Overall))
knitr::kable(most_important_params)
| Overall | |
|---|---|
Population ages 65 and above (% of total population)
|
100.0000000 |
Primary school starting age (years)
|
80.8563320 |
Population density (people per sq. km of land area)
|
53.4587993 |
Net domestic credit (current LCU)
|
52.6590768 |
Taxes on exports (current LCU)
|
48.9759980 |
Population in largest city
|
48.0025427 |
Rural population
|
41.0611870 |
Population in the largest city (% of urban population)
|
37.6692712 |
Population, male
|
37.2659936 |
Gross domestic savings (current US$)
|
36.2706494 |
Urban population (% of total population)
|
35.1373015 |
GDP (current US$)
|
33.8950079 |
Birth rate, crude (per 1,000 people)
|
33.8443819 |
Population, male (% of total population)
|
32.5771095 |
Population, female
|
32.3449556 |
Population ages 0-14 (% of total population)
|
31.6577552 |
Net primary income (Net income from abroad) (current LCU)
|
27.5124392 |
| Long.Interest.Rate | 26.7542316 |
Employment in services (% of total employment) (modeled ILO estimate)
|
26.1037024 |
| CPI | 25.3032190 |
Pupil-teacher ratio, primary
|
24.5393840 |
Imports of goods and services (current US$)
|
22.1230091 |
Urban population growth (annual %)
|
5.6790936 |
Service imports (BoP, current US$)
|
5.3768187 |
Mortality rate, infant (per 1,000 live births)
|
5.1951032 |
Unemployment, total (% of total labor force) (national estimate)
|
4.8525022 |
Rural population (% of total population)
|
4.7604971 |
Access to electricity (% of population)
|
4.5272803 |
CO2 emissions (kg per 2017 PPP $ of GDP)
|
3.8979282 |
Population growth (annual %)
|
3.4443390 |
Gross national expenditure (current US$)
|
3.4015567 |
Pupil-teacher ratio, preprimary
|
3.2125578 |
Population, female (% of total population)
|
3.1409353 |
Lending interest rate (%)
|
3.1264868 |
Trade in services (% of GDP)
|
2.9771743 |
CO2 emissions (kg per 2010 US$ of GDP)
|
2.8247200 |
Transport services (% of commercial service exports)
|
2.7315317 |
Short-term debt (% of total reserves)
|
2.6961226 |
Population in urban agglomerations of more than 1 million
|
2.6911832 |
Exports of goods and services (current US$)
|
2.3995592 |
Trademark applications, direct resident
|
2.3926987 |
Secondary education, teachers
|
2.3431786 |
Net acquisition of financial assets (% of GDP)
|
2.2098728 |
Taxes less subsidies on products (current LCU)
|
2.1910271 |
Patent applications, nonresidents
|
2.1796672 |
Employment in agriculture (% of total employment) (modeled ILO estimate)
|
2.1567295 |
Electricity production from nuclear sources (% of total)
|
2.0904594 |
Net primary income (Net income from abroad) (constant LCU)
|
1.9632592 |
CO2 intensity (kg per kg of oil equivalent energy use)
|
1.9289845 |
Interest payments (% of expense)
|
1.8619920 |
Imports of goods and services (% of GDP)
|
1.8421177 |
Net primary income (Net income from abroad) (current US$)
|
1.8089223 |
Pupil-teacher ratio, upper secondary
|
1.7212088 |
CO2 emissions from manufacturing industries and construction (% of total fuel combustion)
|
1.7011348 |
Pupil-teacher ratio, tertiary
|
1.6658654 |
Individuals using the Internet (% of population)
|
1.6390809 |
Portfolio investment, bonds (PPG + PNG) (NFL, current US$)
|
1.5924655 |
Gross national expenditure (% of GDP)
|
1.5190008 |
Taxes on income, profits and capital gains (% of revenue)
|
1.3958916 |
Survival to age 65, male (% of cohort)
|
1.3778093 |
Trademark applications, direct nonresident
|
1.3613775 |
Deposit interest rate (%)
|
1.3581518 |
Total natural resources rents (% of GDP)
|
1.3432132 |
Service exports (BoP, current US$)
|
1.3173941 |
Total greenhouse gas emissions (kt of CO2 equivalent)
|
1.3158992 |
Self-employed, total (% of total employment) (modeled ILO estimate)
|
1.2807738 |
Self-employed, male (% of male employment) (modeled ILO estimate)
|
1.2583799 |
Urban population
|
1.1917421 |
Natural gas rents (% of GDP)
|
1.1894019 |
GDP per capita (current US$)
|
1.1338543 |
Primary income receipts (BoP, current US$)
|
1.0131950 |
Fuel imports (% of merchandise imports)
|
0.9454639 |
Manufacturing, value added (% of GDP)
|
0.9150827 |
Taxes on income, profits and capital gains (% of total taxes)
|
0.9039033 |
Real interest rate (%)
|
0.8409638 |
Electricity production from hydroelectric sources (% of total)
|
0.7705298 |
Trade (% of GDP)
|
0.7529782 |
Taxes on international trade (% of revenue)
|
0.6953153 |
School enrollment, tertiary (gross), gender parity index (GPI)
|
0.6749071 |
Self-employed, female (% of female employment) (modeled ILO estimate)
|
0.6441500 |
Electricity production from renewable sources, excluding hydroelectric (kWh)
|
0.6166715 |
Gross domestic savings (% of GDP)
|
0.5374528 |
Taxes on international trade (current LCU)
|
0.5279232 |
Short-term debt (% of total external debt)
|
0.4550172 |
Methane emissions in energy sector (thousand metric tons of CO2 equivalent)
|
0.4499030 |
Patent applications, residents
|
0.4116928 |
External debt stocks (% of GNI)
|
0.3804283 |
Share of youth not in education, employment or training, total (% of youth population)
|
0.3759905 |
| S.P.Composite | 0.3546983 |
Food exports (% of merchandise exports)
|
0.3286586 |
Taxes on goods and services (current LCU)
|
0.2735620 |
Tax revenue (% of GDP)
|
0.2619465 |
CO2 emissions from solid fuel consumption (kt)
|
0.2567704 |
CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)
|
0.2468928 |
Population ages 15-64 (% of total population)
|
0.2458792 |
Short-term debt (% of exports of goods, services and primary income)
|
0.2289043 |
GDP per capita growth (annual %)
|
0.2269565 |
Taxes on goods and services (% of revenue)
|
0.2118874 |
| Cyclically.Adjusted.PE.Ratio | 0.1937372 |
Number of under-five deaths
|
0.1764525 |
Trademark applications, total
|
0.1732389 |
Taxes on exports (% of tax revenue)
|
0.1628117 |
| Real.Earnings | 0.1574525 |
CO2 emissions from transport (% of total fuel combustion)
|
0.1419731 |
CO2 emissions from liquid fuel consumption (kt)
|
0.1418419 |
Life expectancy at birth, total (years)
|
0.1410901 |
Literacy rate, adult total (% of people ages 15 and above)
|
0.1212869 |
Electricity production from renewable sources, excluding hydroelectric (% of total)
|
0.1102551 |
CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)
|
0.0976779 |
Stocks traded, total value (% of GDP)
|
0.0932381 |
CO2 emissions (kt)
|
0.0806734 |
CO2 emissions from gaseous fuel consumption (% of total)
|
0.0803164 |
Methane emissions (kt of CO2 equivalent)
|
0.0785596 |
Gross savings (current US$)
|
0.0742113 |
| Earnings | 0.0658151 |
GNI growth (annual %)
|
0.0623997 |
Goods imports (BoP, current US$)
|
0.0574973 |
Renewable electricity output (% of total electricity output)
|
0.0562162 |
CO2 emissions (metric tons per capita)
|
0.0560265 |
| Dividend | 0.0555865 |
Taxes on goods and services (% value added of industry and services)
|
0.0555147 |
Goods exports (BoP, current US$)
|
0.0476203 |
Net primary income (BoP, current US$)
|
0.0442267 |
Taxes less subsidies on products (constant LCU)
|
0.0430038 |
Share of youth not in education, employment or training, male (% of male youth population)
|
0.0370544 |
Net official development assistance received (current US$)
|
0.0313795 |
Income share held by highest 10%
|
0.0290213 |
Inflation, consumer prices (annual %)
|
0.0277853 |
Part time employment, total (% of total employment)
|
0.0272075 |
Stocks traded, turnover ratio of domestic shares (%)
|
0.0234348 |
Primary income payments (BoP, current US$)
|
0.0233998 |
Portfolio investment, net (BoP, current US$)
|
0.0219749 |
Electricity production from coal sources (% of total)
|
0.0210960 |
| Real.Price | 0.0196614 |
Merchandise exports to high-income economies (% of total merchandise exports)
|
0.0134215 |
CO2 emissions from gaseous fuel consumption (kt)
|
0.0102393 |
S&P Global Equity Indices (annual % change)
|
0.0095169 |
CO2 emissions from liquid fuel consumption (% of total)
|
0.0056824 |
Services, value added (% of GDP)
|
0.0054789 |
Land area (sq. km)
|
0.0048623 |
Taxes less subsidies on products (current US$)
|
0.0045670 |
Taxes on income, profits and capital gains (current LCU)
|
0.0045075 |
Electricity production from natural gas sources (% of total)
|
0.0033883 |
Portfolio equity, net inflows (BoP, current US$)
|
0.0026918 |
Tax revenue (current LCU)
|
0.0019399 |
Secondary education, pupils
|
0.0001423 |
CO2 emissions from electricity and heat production, total (% of total fuel combustion)
|
0.0001411 |
CO2 emissions (kg per PPP $ of GDP)
|
0.0000000 |
CO2 emissions from solid fuel consumption (% of total)
|
0.0000000 |
Consumer price index (2010 = 100)
|
0.0000000 |
Electricity production from oil, gas and coal sources (% of total)
|
0.0000000 |
Employers, total (% of total employment) (modeled ILO estimate)
|
0.0000000 |
Employment in industry (% of total employment) (modeled ILO estimate)
|
0.0000000 |
Expense (% of GDP)
|
0.0000000 |
Exports of goods and services (annual % growth)
|
0.0000000 |
Food imports (% of merchandise imports)
|
0.0000000 |
Fuel exports (% of merchandise exports)
|
0.0000000 |
GDP growth (annual %)
|
0.0000000 |
Government expenditure on education, total (% of GDP)
|
0.0000000 |
Gross savings (% of GDP)
|
0.0000000 |
Labor force, total
|
0.0000000 |
Nitrous oxide emissions (thousand metric tons of CO2 equivalent)
|
0.0000000 |
Nitrous oxide emissions in energy sector (% of total)
|
0.0000000 |
Population, total
|
0.0000000 |
Pupil-teacher ratio, secondary
|
0.0000000 |
Renewable energy consumption (% of total final energy consumption)
|
0.0000000 |
Rural population growth (annual %)
|
0.0000000 |
Share of youth not in education, employment or training, female (% of female youth population)
|
0.0000000 |
Stocks traded, total value (current US$)
|
0.0000000 |
Survival to age 65, female (% of cohort)
|
0.0000000 |
Transport services (% of commercial service imports)
|
0.0000000 |
Unemployment with advanced education (% of total labor force with advanced education)
|
0.0000000 |
| Real.Dividend | 0.0000000 |
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.